/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib/__pycache__/
/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib/__pycache__
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Edit: /opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/lib/__pycache__/npyio.cpython-311.pyc (105784B)
WpddlZddlZddlZddlZddlZddlZddlZddlZddlmZm Z m Z ddl m Z ddlZddlmZddlmZddlmZddlmZmZdd lmZdd lmZmZdd lmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)dd l*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0gd Z1ej2ej3dZ3GddZ4dZ5Gdde Z6ed d?ej7ddZ8d@dZ9e3e9dAdZ:dZ;e3e;dZdBd Z?d!Z@d"eAfd#ZBd$ZCdCd&ZDd'ZEd$ZCd(d)d*d+ddddddejFd,d- d.ZGeedeHd)ddddddd,df ddd/d0ZIe3eIZJ dDd1ZKe3eK dEd7ZLeddFd8ZMeedeHd)dddddddddd5NeOe jPd9ddd:dddddd,fddd;d<ZQe3eQZRd=ZSd>ZTdS)GN) itemgetterindex methodcaller)Mapping)format) DataSource) overrides)packbits unpackbits)_load_from_filelike)set_array_function_like_doc set_module) LineSplitter NameValidatorStringConverterConverterErrorConverterLockErrorConversionWarning_is_string_likehas_nested_fields flatten_dtype easy_dtype _decode_line)asbytesasstr asunicode os_fspath os_PathLikepickle) savetxtloadtxt genfromtxt recfromtxt recfromcsvloadsavesavezsavez_compressedr r fromregexr numpy)modulec$eZdZdZdZdZdZdS)BagObjam BagObj(obj) Convert attribute look-ups to getitems on the object passed in. Parameters ---------- obj : class instance Object on which attribute look-up is performed. Examples -------- >>> from numpy.lib.npyio import BagObj as BO >>> class BagDemo: ... def __getitem__(self, key): # An instance of BagObj(BagDemo) ... # will call this method when any ... # attribute look-up is required ... result = "Doesn't matter what you want, " ... return result + "you're gonna get this" ... >>> demo_obj = BagDemo() >>> bagobj = BO(demo_obj) >>> bagobj.hello_there "Doesn't matter what you want, you're gonna get this" >>> bagobj.I_can_be_anything "Doesn't matter what you want, you're gonna get this" c8tj||_dSN)weakrefproxy_obj)selfobjs c/builddir/build/BUILD/cloudlinux-venv-1.0.12/venv/lib64/python3.11/site-packages/numpy/lib/npyio.py__init__zBagObj.__init__HsM#&& c t|d|S#t$rt|dwxYw)Nr3)object__getattribute__KeyErrorAttributeErrorr4keys r6r;zBagObj.__getattribute__LsM 0**488= = 0 0 0 %%4 / 0s #>cvtt|dS)z Enables dir(bagobj) to list the files in an NpzFile. This also enables tab-completion in an interpreter or IPython. r3)listr:r;keysr4s r6__dir__zBagObj.__dir__Rs. F++D&99>>@@AAAr8N)__name__ __module__ __qualname____doc__r7r;rDr8r6r.r.*sP:'''000 BBBBBr8r.ctt|dst|}ddl}d|d<|j|g|Ri|S)z Create a ZipFile. Allows for Zip64, and the `file` argument can accept file, str, or pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile constructor. readrNT allowZip64)hasattrrzipfileZipFile)fileargskwargsrNs r6zipfile_factoryrS[sV 4 NNNF< 7?4 1$ 1 1 1& 1 11r8cpeZdZdZdZdZdZ dejddZ dZ dZ d Z d Z d Zd Zd ZdZdZdS)NpzFilea, NpzFile(fid) A dictionary-like object with lazy-loading of files in the zipped archive provided on construction. `NpzFile` is used to load files in the NumPy ``.npz`` data archive format. It assumes that files in the archive have a ``.npy`` extension, other files are ignored. The arrays and file strings are lazily loaded on either getitem access using ``obj['key']`` or attribute lookup using ``obj.f.key``. A list of all files (without ``.npy`` extensions) can be obtained with ``obj.files`` and the ZipFile object itself using ``obj.zip``. Attributes ---------- files : list of str List of all files in the archive with a ``.npy`` extension. zip : ZipFile instance The ZipFile object initialized with the zipped archive. f : BagObj instance An object on which attribute can be performed as an alternative to getitem access on the `NpzFile` instance itself. allow_pickle : bool, optional Allow loading pickled data. Default: False .. versionchanged:: 1.16.3 Made default False in response to CVE-2019-6446. pickle_kwargs : dict, optional Additional keyword arguments to pass on to pickle.load. These are only useful when loading object arrays saved on Python 2 when using Python 3. max_header_size : int, optional Maximum allowed size of the header. Large headers may not be safe to load securely and thus require explicitly passing a larger value. See :py:func:`ast.literal_eval()` for details. This option is ignored when `allow_pickle` is passed. In that case the file is by definition trusted and the limit is unnecessary. Parameters ---------- fid : file or str The zipped archive to open. This is either a file-like object or a string containing the path to the archive. own_fid : bool, optional Whether NpzFile should close the file handle. Requires that `fid` is a file-like object. Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> y = np.sin(x) >>> np.savez(outfile, x=x, y=y) >>> _ = outfile.seek(0) >>> npz = np.load(outfile) >>> isinstance(npz, np.lib.npyio.NpzFile) True >>> npz NpzFile 'object' with keys x, y >>> sorted(npz.files) ['x', 'y'] >>> npz['x'] # getitem access array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> npz.f.x # attribute lookup array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) NFmax_header_sizect|}||_g|_||_||_||_|jD]T}|dr#|j|dd:|j|U||_ t||_ |r ||_ dSdS)N.npy) rSnamelist_filesfiles allow_picklerX pickle_kwargsendswithappendzipr.ffid)r4reown_fidr_r`rX_zipxs r6r7zNpzFile.__init__s s##mmoo  (.* % %Azz&!! % !!!CRC&)))) !!!$$$$  DHHH  r8c|Sr0rIrCs r6 __enter__zNpzFile.__enter__s r8c.|dSr0close)r4exc_type exc_value tracebacks r6__exit__zNpzFile.__exit__ r8c|j |jd|_|j |jd|_d|_dS)z" Close the file. N)rcrmrerdrCs r6rmz NpzFile.closesQ 8  HNN   DH 8  HNN   DHr8c.|dSr0rlrCs r6__del__zNpzFile.__del__rrr8c*t|jSr0)iterr^rCs r6__iter__zNpzFile.__iter__sDJr8c*t|jSr0)lenr^rCs r6__len__zNpzFile.__len__s4:r8cd}||jvrd}n||jvrd}|dz }|r|j|}|t t j}||t jkrA|j|}t j ||j |j |j S|j|St|d)NFTrZr_r`rXz is not a file in the archive)r]r^rcopenrKrzr MAGIC_PREFIXrm read_arrayr_r`rXr<)r4r?memberbytesmagics r6 __getitem__zNpzFile.__getitem__s $+  FF DJ  F 6MC  BHMM#&&EJJs6#67788E KKMMM+++ c**(6:6G7;7I9=9MOOOO x}}S)))c@@@AA Ar8c&||jvp||jvSr0)r]r^r>s r6 __contains__zNpzFile.__contains__ st{"7cTZ&78r8ct|jtr|j}nt|jdd}d|jd|j}t|j|jkr|dz }d|d|S)Nnamer:z, z...zNpzFile z with keys: ) isinstancerestrgetattrjoinr^_MAX_REPR_ARRAY_COUNTrz)r4filename array_namess r6__repr__zNpzFile.__repr__ s dh $ $ ;xHHtx::Hii +FD,F+F GHH tz??T7 7 7 5 K?(??+???r8)FFN)rErFrGrHrcrerr_MAX_HEADER_SIZEr7rjrqrmrurxr{rrrrIr8r6rUrUjsHHT C C8=#!'!8*      BBB<999 @ @ @ @ @r8rUFTASCIIrWc 4|dvrtdt||}tj5}t |dr|}d} n2|t t|d}d} d} d } ttj } | | } | std | t| t|  d | | s| | r6|t#|| ||| }|cd d d S| tj krM|r'|rd}tj|||cd d d Stj||||cd d d S|std t)j|fi|cd d d S#t,$r}t)jd|d|d }~wwxYw#1swxYwYd S)a0 Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files. .. warning:: Loading files that contain object arrays uses the ``pickle`` module, which is not secure against erroneous or maliciously constructed data. Consider passing ``allow_pickle=False`` to load data that is known not to contain object arrays for the safer handling of untrusted sources. Parameters ---------- file : file-like object, string, or pathlib.Path The file to read. File-like objects must support the ``seek()`` and ``read()`` methods and must always be opened in binary mode. Pickled files require that the file-like object support the ``readline()`` method as well. mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional If not None, then memory-map the file, using the given mode (see `numpy.memmap` for a detailed description of the modes). A memory-mapped array is kept on disk. However, it can be accessed and sliced like any ndarray. Memory mapping is especially useful for accessing small fragments of large files without reading the entire file into memory. allow_pickle : bool, optional Allow loading pickled object arrays stored in npy files. Reasons for disallowing pickles include security, as loading pickled data can execute arbitrary code. If pickles are disallowed, loading object arrays will fail. Default: False .. versionchanged:: 1.16.3 Made default False in response to CVE-2019-6446. fix_imports : bool, optional Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. If `fix_imports` is True, pickle will try to map the old Python 2 names to the new names used in Python 3. encoding : str, optional What encoding to use when reading Python 2 strings. Only useful when loading Python 2 generated pickled files in Python 3, which includes npy/npz files containing object arrays. Values other than 'latin1', 'ASCII', and 'bytes' are not allowed, as they can corrupt numerical data. Default: 'ASCII' max_header_size : int, optional Maximum allowed size of the header. Large headers may not be safe to load securely and thus require explicitly passing a larger value. See :py:func:`ast.literal_eval()` for details. This option is ignored when `allow_pickle` is passed. In that case the file is by definition trusted and the limit is unnecessary. Returns ------- result : array, tuple, dict, etc. Data stored in the file. For ``.npz`` files, the returned instance of NpzFile class must be closed to avoid leaking file descriptors. Raises ------ OSError If the input file does not exist or cannot be read. UnpicklingError If ``allow_pickle=True``, but the file cannot be loaded as a pickle. ValueError The file contains an object array, but ``allow_pickle=False`` given. EOFError When calling ``np.load`` multiple times on the same file handle, if all data has already been read See Also -------- save, savez, savez_compressed, loadtxt memmap : Create a memory-map to an array stored in a file on disk. lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file. Notes ----- - If the file contains pickle data, then whatever object is stored in the pickle is returned. - If the file is a ``.npy`` file, then a single array is returned. - If the file is a ``.npz`` file, then a dictionary-like object is returned, containing ``{filename: array}`` key-value pairs, one for each file in the archive. - If the file is a ``.npz`` file, the returned value supports the context manager protocol in a similar fashion to the open function:: with load('foo.npz') as data: a = data['a'] The underlying file descriptor is closed when exiting the 'with' block. Examples -------- Store data to disk, and load it again: >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]])) >>> np.load('/tmp/123.npy') array([[1, 2, 3], [4, 5, 6]]) Store compressed data to disk, and load it again: >>> a=np.array([[1, 2, 3], [4, 5, 6]]) >>> b=np.array([1, 2]) >>> np.savez('/tmp/123.npz', a=a, b=b) >>> data = np.load('/tmp/123.npz') >>> data['a'] array([[1, 2, 3], [4, 5, 6]]) >>> data['b'] array([1, 2]) >>> data.close() Mem-map the stored array, and then access the second row directly from disk: >>> X = np.load('/tmp/123.npy', mmap_mode='r') >>> X[1, :] memmap([4, 5, 6]) )rlatin1rz.encoding must be 'ASCII', 'latin1', or 'bytes')encoding fix_importsrKFrbTsPKsPKzNo data left in filer)rfr_r`rXNl)moderXr}z@Cannot load file containing pickled data when allow_pickle=FalsezFailed to interpret file z as a pickle) ValueErrordict contextlib ExitStackrM enter_contextr~rrzrrrKEOFErrorseekmin startswithpop_allrU open_memmaprr r& ExceptionUnpicklingError)rP mmap_moder_rrrXr`stackrerf _ZIP_PREFIX _ZIP_SUFFIXNrretes r6r&r&sRx333IJJJ( DDDM    .M5 4  CGG%%d9T??D&A&ABBCG$ # # $ $  3122 2 #aU$$$a(((   K ( ( ME,<,<[,I,I M MMOOO#w\(5*9;;;C3.M.M.M.M.M.M.M.M4f) ) ) J,&+O)$Y:IKKK?.M.M.M.M.M.M.M.MD(<7D9HJJJE.M.M.M.M.M.M.M.MN  < ";<<< M{388-88W.M.M.M.M.M.M.M.MX M M M,DDDDFFKLM MY.M.M.M.M.M.M.M.M.M.MsBDH ,H H 2H G"" H ,HH  H  HHc|fSr0rI)rParrr_rs r6_save_dispatcherrs 6Mr8c lt|drtj|}n9t|}|ds|dz}t |d}|5}t j|}tj |||t|ddddS#1swxYwYdS)a< Save an array to a binary file in NumPy ``.npy`` format. Parameters ---------- file : file, str, or pathlib.Path File or filename to which the data is saved. If file is a file-object, then the filename is unchanged. If file is a string or Path, a ``.npy`` extension will be appended to the filename if it does not already have one. arr : array_like Array data to be saved. allow_pickle : bool, optional Allow saving object arrays using Python pickles. Reasons for disallowing pickles include security (loading pickled data can execute arbitrary code) and portability (pickled objects may not be loadable on different Python installations, for example if the stored objects require libraries that are not available, and not all pickled data is compatible between Python 2 and Python 3). Default: True fix_imports : bool, optional Only useful in forcing objects in object arrays on Python 3 to be pickled in a Python 2 compatible way. If `fix_imports` is True, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2. See Also -------- savez : Save several arrays into a ``.npz`` archive savetxt, load Notes ----- For a description of the ``.npy`` format, see :py:mod:`numpy.lib.format`. Any data saved to the file is appended to the end of the file. Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> np.save(outfile, x) >>> _ = outfile.seek(0) # Only needed here to simulate closing & reopening file >>> np.load(outfile) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> with open('test.npy', 'wb') as f: ... np.save(f, np.array([1, 2])) ... np.save(f, np.array([1, 3])) >>> with open('test.npy', 'rb') as f: ... a = np.load(f) ... b = np.load(f) >>> print(a, b) # [1 2] [1 3] writerZwb)rr_r`N) rMr nullcontextrrar~np asanyarrayr write_arrayr)rPrr_rfile_ctxres r6r'r'sztW$)$//}}V$$ !&=Dd## HSmC  3,)-+)F)F)F H H H HHHHHHHHHHHHHHHHHHHs!;B))B-0B-c/NK|Ed{V|Ed{VdSr0valuesrPrQkwdss r6_savez_dispatcherr&=OOOOOOO{{}}r8c*t|||ddS)a Save several arrays into a single file in uncompressed ``.npz`` format. Provide arrays as keyword arguments to store them under the corresponding name in the output file: ``savez(fn, x=x, y=y)``. If arrays are specified as positional arguments, i.e., ``savez(fn, x, y)``, their names will be `arr_0`, `arr_1`, etc. Parameters ---------- file : str or file Either the filename (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the ``.npz`` extension will be appended to the filename if it is not already there. args : Arguments, optional Arrays to save to the file. Please use keyword arguments (see `kwds` below) to assign names to arrays. Arrays specified as args will be named "arr_0", "arr_1", and so on. kwds : Keyword arguments, optional Arrays to save to the file. Each array will be saved to the output file with its corresponding keyword name. Returns ------- None See Also -------- save : Save a single array to a binary file in NumPy format. savetxt : Save an array to a file as plain text. savez_compressed : Save several arrays into a compressed ``.npz`` archive Notes ----- The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is not compressed and each file in the archive contains one variable in ``.npy`` format. For a description of the ``.npy`` format, see :py:mod:`numpy.lib.format`. When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the ``.files`` attribute), and for the arrays themselves. Keys passed in `kwds` are used as filenames inside the ZIP archive. Therefore, keys should be valid filenames; e.g., avoid keys that begin with ``/`` or contain ``.``. When naming variables with keyword arguments, it is not possible to name a variable ``file``, as this would cause the ``file`` argument to be defined twice in the call to ``savez``. Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> y = np.sin(x) Using `savez` with \*args, the arrays are saved with default names. >>> np.savez(outfile, x, y) >>> _ = outfile.seek(0) # Only needed here to simulate closing & reopening file >>> npzfile = np.load(outfile) >>> npzfile.files ['arr_0', 'arr_1'] >>> npzfile['arr_0'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Using `savez` with \**kwds, the arrays are saved with the keyword names. >>> outfile = TemporaryFile() >>> np.savez(outfile, x=x, y=y) >>> _ = outfile.seek(0) >>> npzfile = np.load(outfile) >>> sorted(npzfile.files) ['x', 'y'] >>> npzfile['x'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) FN_savezrs r6r(r(+sh 4tU#####r8c/NK|Ed{V|Ed{VdSr0rrs r6_savez_compressed_dispatcherrrr8c*t|||ddS)a Save several arrays into a single file in compressed ``.npz`` format. Provide arrays as keyword arguments to store them under the corresponding name in the output file: ``savez(fn, x=x, y=y)``. If arrays are specified as positional arguments, i.e., ``savez(fn, x, y)``, their names will be `arr_0`, `arr_1`, etc. Parameters ---------- file : str or file Either the filename (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the ``.npz`` extension will be appended to the filename if it is not already there. args : Arguments, optional Arrays to save to the file. Please use keyword arguments (see `kwds` below) to assign names to arrays. Arrays specified as args will be named "arr_0", "arr_1", and so on. kwds : Keyword arguments, optional Arrays to save to the file. Each array will be saved to the output file with its corresponding keyword name. Returns ------- None See Also -------- numpy.save : Save a single array to a binary file in NumPy format. numpy.savetxt : Save an array to a file as plain text. numpy.savez : Save several arrays into an uncompressed ``.npz`` file format numpy.load : Load the files created by savez_compressed. Notes ----- The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is compressed with ``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable in ``.npy`` format. For a description of the ``.npy`` format, see :py:mod:`numpy.lib.format`. When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the ``.files`` attribute), and for the arrays themselves. Examples -------- >>> test_array = np.random.rand(3, 2) >>> test_vector = np.random.rand(4) >>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector) >>> loaded = np.load('/tmp/123.npz') >>> print(np.array_equal(test_array, loaded['a'])) True >>> print(np.array_equal(test_vector, loaded['b'])) True TNrrs r6r)r)s~ 4tT"""""r8cvddl}t|ds)t|}|ds|dz}|}t |D]7\}} d|z} | |vrt d| z| || <8|r|j} n|j} t|d| } | D]f\} } | dz} tj | } | | dd 5}tj|| || dddn #1swxYwYg| dS) Nrrz.npzzarr_%dz,Cannot use un-named variables and keyword %sw)r compressionrZT) force_zip64r)rNrMrra enumeraterBr ZIP_DEFLATED ZIP_STOREDrSitemsrrr~rrrm)rPrQrcompressr_r`rNnamedictivalr?rzipffnameres r6rrsNNN 4 ! !!}}V$$ !&=DHD//3l (--// ! !>DFF F )* ( 4c{ C C CDNN$$<<Sf mC   YYuctY 4 4 <  sC,8-: < < < < < < < < < < < < < < < < < < <  JJLLLLLs4DD D c2|dvrtd|dS)zJust checks if the param ndmin is supported on _ensure_ndmin_ndarray. It is intended to be used as verification before running anything expensive. e.g. loadtxt, genfromtxt )rrz Illegal value of ndmin keyword: N)rndmins r6!_ensure_ndmin_ndarray_check_paramrs. ICECCDDDr8rc|j|krtj|}|j|kr:|dkrtj|}n|dkrtj|j}|S)aThis is a helper function of loadtxt and genfromtxt to ensure proper minimum dimension as requested ndim : int. Supported values 1, 2, 3 ^^ whenever this changes, keep in sync with _ensure_ndmin_ndarray_check_param rr)ndimrsqueeze atleast_1d atleast_2dT)ars r6_ensure_ndmin_ndarrayrsc v~~ JqMM v~~ A:: a  AA aZZ a  "A Hr8iPargumentc tj|n!#t$rt|ddwxYw|dkrt|ddS)Nz must be an integerrz must be nonnegative)operatorr TypeErrorr)valuers r6_check_nonneg_intrsz@u @@@4444554?@ qyyD666777ys5c#K|D]Q}t|tr||}|D]}||dd}|VRdS)a Generator that consumes a line iterated iterable and strips out the multiple (or multi-character) comments from lines. This is a pre-processing step to achieve feature parity with loadtxt (we assume that this feature is a nieche feature). rrN)rrdecodesplit)iterablecommentsrlinecs r6_preprocess_commentsrsx dE " " );;x((D ' 'A::a##A&DD r8,#"jr) delimitercommentquoteimaginary_unitusecols skiplinesmax_rows convertersrunpackdtyperc & d} | dkrd} d} | tdtj| } d}| jdvr3| dks| dks | d ks| d kr| }tjt} |$ t |}n#t$r|g}YnwxYwt | |d}nd |vrtd t|}d}t|d krd}nlt|dkr?t|d tr#t|d dkr |d }d}n||vrtd|d|d||tdt|dkrtdt||t|nd}tj}d} t|tjrtj|}t|trStjj|d| }| t+|dd} tj|}|}d}n"| t+|dd} t/|}n3#t$r&}tdt1|d|d}~wwxYw|5|$|rt/|}d}t3||| }|t5|||||||||| | ||  n|rt/|}d}|dkrd}g}|d kr|d krt6}nt9t6|}t5|||||||||| | || |}|||d }|d kr||z}t||krn|d kt|dkrt|dd kr|d=t|dkr |d ntj|d dddn #1swxYwYtA| j!r1j!d d kr tEj#d |d!tHd"#| r(j}|j%fd$|j%DSj&SS)%a Read a NumPy array from a text file. Parameters ---------- fname : str or file object The filename or the file to be read. delimiter : str, optional Field delimiter of the fields in line of the file. Default is a comma, ','. If None any sequence of whitespace is considered a delimiter. comment : str or sequence of str or None, optional Character that begins a comment. All text from the comment character to the end of the line is ignored. Multiple comments or multiple-character comment strings are supported, but may be slower and `quote` must be empty if used. Use None to disable all use of comments. quote : str or None, optional Character that is used to quote string fields. Default is '"' (a double quote). Use None to disable quote support. imaginary_unit : str, optional Character that represent the imaginay unit `sqrt(-1)`. Default is 'j'. usecols : array_like, optional A one-dimensional array of integer column numbers. These are the columns from the file to be included in the array. If this value is not given, all the columns are used. skiplines : int, optional Number of lines to skip before interpreting the data in the file. max_rows : int, optional Maximum number of rows of data to read. Default is to read the entire file. converters : dict or callable, optional A function to parse all columns strings into the desired value, or a dictionary mapping column number to a parser function. E.g. if column 0 is a date string: ``converters = {0: datestr2num}``. Converters can also be used to provide a default value for missing data, e.g. ``converters = lambda s: float(s.strip() or 0)`` will convert empty fields to 0. Default: None ndmin : int, optional Minimum dimension of the array returned. Allowed values are 0, 1 or 2. Default is 0. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = read(...)``. When used with a structured data-type, arrays are returned for each field. Default is False. dtype : numpy data type A NumPy dtype instance, can be a structured dtype to map to the columns of the file. encoding : str, optional Encoding used to decode the inputfile. The special value 'bytes' (the default) enables backwards-compatible behavior for `converters`, ensuring that inputs to the converter functions are encoded bytes objects. The special value 'bytes' has no additional effect if ``converters=None``. If encoding is ``'bytes'`` or ``None``, the default system encoding is used. Returns ------- ndarray NumPy array. Examples -------- First we create a file for the example. >>> s1 = '1.0,2.0,3.0\n4.0,5.0,6.0\n' >>> with open('example1.csv', 'w') as f: ... f.write(s1) >>> a1 = read_from_filename('example1.csv') >>> a1 array([[1., 2., 3.], [4., 5., 6.]]) The second example has columns with different data types, so a one-dimensional array with a structured data type is returned. The tab character is used as the field delimiter. >>> s2 = '1.0\t10\talpha\n2.3\t25\tbeta\n4.5\t16\tgamma\n' >>> with open('example2.tsv', 'w') as f: ... f.write(s2) >>> a2 = read_from_filename('example2.tsv', delimiter='\t') >>> a2 array([(1. , 10, b'alpha'), (2.3, 25, b'beta'), (4.5, 16, b'gamma')], dtype=[('f0', 'z_read..Cs5555CJ555r8)'rrrkindr:rArrtuplerzrrrrrosPathLikefspathlib _datasourcer~rclosingrwtyperr _loadtxt_chunksizerrbastype concatenatershapewarningswarn UserWarningnamesr)rrrrrrrrrrrrrr read_dtype_via_object_chunksrfh_closing_ctxr fhdatarr chunks chunk_sizenext_arrskiprowsdtrs @r6_readr/2sxO7 }3444 HUOOE#'  zU TMMUd]]etmmu}}(-$   7mmGG   iGGG &e,,, ==$ >> x==A  HH ]]a  (1+s++ HQK0@0@A0E0E"1+H$$/8//"+///   '(( (  >a9:::i   (####+--NH@ eR[ ) ) %Ie$$E eS ! ! #((x(HHB"2z8<<'/33NDHH"5*h??;;D @@@ 8!%e 8 8 899>? @@ >5>5   !Dzz 'hAAD ' /% 7%-9x%U!H / 111CC "Dzz % +s22$(!Fa--a<5>5>5>5>5>5>5>5>5>5>5>5>5>5>5H 5 1 1 1C y 9Q<1   M>e>>>$       Y 8 5555BH555 55L s>4B BB?B8I88 J(!J##J(-EPPP) quotecharlikec | t| |||||||||| |  St|tr|d| tj}|} | +t| t tfr| g} d| D} t|tr|d}t||| ||||||| | |  }|S)a' Load data from a text file. Parameters ---------- fname : file, str, pathlib.Path, list of str, generator File, filename, list, or generator to read. If the filename extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note that generators must return bytes or strings. The strings in a list or produced by a generator are treated as lines. dtype : data-type, optional Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array. In this case, the number of columns used must match the number of fields in the data-type. comments : str or sequence of str or None, optional The characters or list of characters used to indicate the start of a comment. None implies no comments. For backwards compatibility, byte strings will be decoded as 'latin1'. The default is '#'. delimiter : str, optional The character used to separate the values. For backwards compatibility, byte strings will be decoded as 'latin1'. The default is whitespace. .. versionchanged:: 1.23.0 Only single character delimiters are supported. Newline characters cannot be used as the delimiter. converters : dict or callable, optional Converter functions to customize value parsing. If `converters` is callable, the function is applied to all columns, else it must be a dict that maps column number to a parser function. See examples for further details. Default: None. .. versionchanged:: 1.23.0 The ability to pass a single callable to be applied to all columns was added. skiprows : int, optional Skip the first `skiprows` lines, including comments; default: 0. usecols : int or sequence, optional Which columns to read, with 0 being the first. For example, ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns. The default, None, results in all columns being read. .. versionchanged:: 1.11.0 When a single column has to be read it is possible to use an integer instead of a tuple. E.g ``usecols = 3`` reads the fourth column the same way as ``usecols = (3,)`` would. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = loadtxt(...)``. When used with a structured data-type, arrays are returned for each field. Default is False. ndmin : int, optional The returned array will have at least `ndmin` dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2. .. versionadded:: 1.6.0 encoding : str, optional Encoding used to decode the inputfile. Does not apply to input streams. The special value 'bytes' enables backward compatibility workarounds that ensures you receive byte arrays as results if possible and passes 'latin1' encoded strings to converters. Override this value to receive unicode arrays and pass strings as input to converters. If set to None the system default is used. The default value is 'bytes'. .. versionadded:: 1.14.0 max_rows : int, optional Read `max_rows` rows of content after `skiprows` lines. The default is to read all the rows. Note that empty rows containing no data such as empty lines and comment lines are not counted towards `max_rows`, while such lines are counted in `skiprows`. .. versionadded:: 1.16.0 .. versionchanged:: 1.23.0 Lines containing no data, including comment lines (e.g., lines starting with '#' or as specified via `comments`) are not counted towards `max_rows`. quotechar : unicode character or None, optional The character used to denote the start and end of a quoted item. Occurrences of the delimiter or comment characters are ignored within a quoted item. The default value is ``quotechar=None``, which means quoting support is disabled. If two consecutive instances of `quotechar` are found within a quoted field, the first is treated as an escape character. See examples. .. versionadded:: 1.23.0 ${ARRAY_FUNCTION_LIKE} .. versionadded:: 1.20.0 Returns ------- out : ndarray Data read from the text file. See Also -------- load, fromstring, fromregex genfromtxt : Load data with missing values handled as specified. scipy.io.loadmat : reads MATLAB data files Notes ----- This function aims to be a fast reader for simply formatted files. The `genfromtxt` function provides more sophisticated handling of, e.g., lines with missing values. Each row in the input text file must have the same number of values to be able to read all values. If all rows do not have same number of values, a subset of up to n columns (where n is the least number of values present in all rows) can be read by specifying the columns via `usecols`. .. versionadded:: 1.10.0 The strings produced by the Python float.hex method can be used as input for floats. Examples -------- >>> from io import StringIO # StringIO behaves like a file object >>> c = StringIO("0 1\n2 3") >>> np.loadtxt(c) array([[0., 1.], [2., 3.]]) >>> d = StringIO("M 21 72\nF 35 58") >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'), ... 'formats': ('S1', 'i4', 'f4')}) array([(b'M', 21, 72.), (b'F', 35, 58.)], dtype=[('gender', 'S1'), ('age', '>> c = StringIO("1,0,2\n3,0,4") >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True) >>> x array([1., 3.]) >>> y array([2., 4.]) The `converters` argument is used to specify functions to preprocess the text prior to parsing. `converters` can be a dictionary that maps preprocessing functions to each column: >>> s = StringIO("1.618, 2.296\n3.141, 4.669\n") >>> conv = { ... 0: lambda x: np.floor(float(x)), # conversion fn for column 0 ... 1: lambda x: np.ceil(float(x)), # conversion fn for column 1 ... } >>> np.loadtxt(s, delimiter=",", converters=conv) array([[1., 3.], [3., 5.]]) `converters` can be a callable instead of a dictionary, in which case it is applied to all columns: >>> s = StringIO("0xDE 0xAD\n0xC0 0xDE") >>> import functools >>> conv = functools.partial(int, base=16) >>> np.loadtxt(s, converters=conv) array([[222., 173.], [192., 222.]]) This example shows how `converters` can be used to convert a field with a trailing minus sign into a negative number. >>> s = StringIO('10.01 31.25-\n19.22 64.31\n17.57- 63.94') >>> def conv(fld): ... return -float(fld[:-1]) if fld.endswith(b'-') else float(fld) ... >>> np.loadtxt(s, converters=conv) array([[ 10.01, -31.25], [ 19.22, 64.31], [-17.57, 63.94]]) Using a callable as the converter can be particularly useful for handling values with different formatting, e.g. floats with underscores: >>> s = StringIO("1 2.7 100_000") >>> np.loadtxt(s, converters=float) array([1.e+00, 2.7e+00, 1.e+05]) This idea can be extended to automatically handle values specified in many different formats: >>> def conv(val): ... try: ... return float(val) ... except ValueError: ... return float.fromhex(val) >>> s = StringIO("1, 2.5, 3_000, 0b4, 0x1.4000000000000p+2") >>> np.loadtxt(s, delimiter=",", converters=conv, encoding=None) array([1.0e+00, 2.5e+00, 3.0e+03, 1.8e+02, 5.0e+00]) Note that with the default ``encoding="bytes"``, the inputs to the converter function are latin-1 encoded byte strings. To deactivate the implicit encoding prior to conversion, use ``encoding=None`` >>> s = StringIO('10.01 31.25-\n19.22 64.31\n17.57- 63.94') >>> conv = lambda x: -float(x[:-1]) if x.endswith('-') else float(x) >>> np.loadtxt(s, converters=conv, encoding=None) array([[ 10.01, -31.25], [ 19.22, 64.31], [-17.57, 63.94]]) Support for quoted fields is enabled with the `quotechar` parameter. Comment and delimiter characters are ignored when they appear within a quoted item delineated by `quotechar`: >>> s = StringIO('"alpha, #42", 10.0\n"beta, #64", 2.0\n') >>> dtype = np.dtype([("label", "U12"), ("value", float)]) >>> np.loadtxt(s, dtype=dtype, delimiter=",", quotechar='"') array([('alpha, #42', 10.), ('beta, #64', 2.)], dtype=[('label', '>> s = StringIO('"alpha, #42" 10.0\n"beta, #64" 2.0\n') >>> dtype = np.dtype([("label", "U12"), ("value", float)]) >>> np.loadtxt(s, dtype=dtype, delimiter=None, quotechar='"') array([('alpha, #42', 10.), ('beta, #64', 2.)], dtype=[('label', '>> s = StringIO('"Hello, my name is ""Monty""!"') >>> np.loadtxt(s, dtype="U", delimiter=",", quotechar='"') array('Hello, my name is "Monty"!', dtype='>> d = StringIO("1 2\n2 4\n3 9 12\n4 16 20") >>> np.loadtxt(d, usecols=(0, 1)) array([[ 1., 2.], [ 2., 4.], [ 3., 9.], [ 4., 16.]]) N) rrrrr-rrrrrrcfg|].}t|tr|dn|/S)r)rrr)rrhs r6rzloadtxt..XsKPPPBC*Q"6"6 =AHHX   APPPr8) rrrrrrrrrrr)_loadtxt_with_likerrrrfloat64rr/)rrrrrr-rrrrrr0r1rrs r6r"r"Jsv ! %ux9!Hg     )U###""" } G gU| , , iGPPGNPPP)U##/$$X.. UGy%7UX! 4 4 4C Jr8c |fSr0rI) rXfmtrnewlineheaderfooterrrs r6_savetxt_dispatcherr<hs  4Kr8%.18e  r# c t|trt|}t|}Gdd} d} t|trt |}t |rLt |dtj j |d|} d} n.t|dr| ||pd} ntd  tj |}|jd ks |jd krtd |jz|jd krB|jjtj|j}d } n't'|jj} n |jd } tj|} t-|t.t0fvrht'|| krt3dt5|zt|t9t|}nt|t4rs|d}td|z}|d kr,| rd|d|dg| z}n|g| z}||}n,| r |d | zkr|| s|| kr||}ntd|t'|d kr4|dd|z}| ||z|z| r|D]{}g}|D]6}| |j!| |j"7|t1|z|z}| |dd|nl|D]i} |t1|z|z}n;#tF$r.}tGdt5|jd|d|d}~wwxYw| |jt'|d kr4|dd|z}| ||z|z| r| dSdS#| r| wwxYw)a Save an array to a text file. Parameters ---------- fname : filename or file handle If the filename ends in ``.gz``, the file is automatically saved in compressed gzip format. `loadtxt` understands gzipped files transparently. X : 1D or 2D array_like Data to be saved to a text file. fmt : str or sequence of strs, optional A single format (%10.5f), a sequence of formats, or a multi-format string, e.g. 'Iteration %d -- %10.5f', in which case `delimiter` is ignored. For complex `X`, the legal options for `fmt` are: * a single specifier, `fmt='%.4e'`, resulting in numbers formatted like `' (%s+%sj)' % (fmt, fmt)` * a full string specifying every real and imaginary part, e.g. `' %.4e %+.4ej %.4e %+.4ej %.4e %+.4ej'` for 3 columns * a list of specifiers, one per column - in this case, the real and imaginary part must have separate specifiers, e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns delimiter : str, optional String or character separating columns. newline : str, optional String or character separating lines. .. versionadded:: 1.5.0 header : str, optional String that will be written at the beginning of the file. .. versionadded:: 1.7.0 footer : str, optional String that will be written at the end of the file. .. versionadded:: 1.7.0 comments : str, optional String that will be prepended to the ``header`` and ``footer`` strings, to mark them as comments. Default: '# ', as expected by e.g. ``numpy.loadtxt``. .. versionadded:: 1.7.0 encoding : {None, str}, optional Encoding used to encode the outputfile. Does not apply to output streams. If the encoding is something other than 'bytes' or 'latin1' you will not be able to load the file in NumPy versions < 1.14. Default is 'latin1'. .. versionadded:: 1.14.0 See Also -------- save : Save an array to a binary file in NumPy ``.npy`` format savez : Save several arrays into an uncompressed ``.npz`` archive savez_compressed : Save several arrays into a compressed ``.npz`` archive Notes ----- Further explanation of the `fmt` parameter (``%[flag]width[.precision]specifier``): flags: ``-`` : left justify ``+`` : Forces to precede result with + or -. ``0`` : Left pad the number with zeros instead of space (see width). width: Minimum number of characters to be printed. The value is not truncated if it has more characters. precision: - For integer specifiers (eg. ``d,i,o,x``), the minimum number of digits. - For ``e, E`` and ``f`` specifiers, the number of digits to print after the decimal point. - For ``g`` and ``G``, the maximum number of significant digits. - For ``s``, the maximum number of characters. specifiers: ``c`` : character ``d`` or ``i`` : signed decimal integer ``e`` or ``E`` : scientific notation with ``e`` or ``E``. ``f`` : decimal floating point ``g,G`` : use the shorter of ``e,E`` or ``f`` ``o`` : signed octal ``s`` : string of characters ``u`` : unsigned decimal integer ``x,X`` : unsigned hexadecimal integer This explanation of ``fmt`` is not complete, for an exhaustive specification see [1]_. References ---------- .. [1] `Format Specification Mini-Language `_, Python Documentation. Examples -------- >>> x = y = z = np.arange(0.0,5.0,1.0) >>> np.savetxt('test.out', x, delimiter=',') # X is an array >>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays >>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation c6eZdZdZdZdZdZdZdZdZ dS) savetxt..WriteWrapz0Convert to bytes on bytestream inputs. c:||_||_|j|_dSr0)r(r first_writedo_write)r4r(rs r6r7z#savetxt..WriteWrap.__init__sDG$DM ,DMMMr8c8|jdSr0)r(rmrCs r6rmz savetxt..WriteWrap.closes GMMOOOOOr8c0||dSr0)rFr4vs r6rz savetxt..WriteWrap.writes MM!     r8ct|tr|j|dS|j||jdSr0)rrr(rencoderrIs r6 write_bytesz&savetxt..WriteWrap.write_bytessV!U## 7 a      ahht}5566666r8cT|jt|dSr0)r(rrrIs r6 write_normalz'savetxt..WriteWrap.write_normals" GMM)A,, ' ' ' ' 'r8c |||j|_dS#t$r%|||j|_YdSwxYwr0)rOrrrMrIs r6rEz&savetxt..WriteWrap.first_writesh .!!!$$$!.  . . .  ###!-  .s!%+AAN) rErFrGrHr7rmrrMrOrErIr8r6 WriteWraprCsx   - - -        7 7 7  ( ( ( . . . . .r8rQFwtrTrrz%fname must be a string or file handlerrz.Expected 1D or 2D array, got %dD array insteadrNzfmt has wrong shape. %s%z'fmt has wrong number of %% formats: %sz (+zj)z invalid fmt: r?z+--zMismatch between array dtype ('z') and format specifier ('z'))$rrrrrrr~rmrrrrMrasarrayrrr%rrrzr! iscomplexobjrrArr=rrmapcountreplacerrbrealimagr)rr7r8rr9r:r;rrrQown_fhr(ncol iscomplex_Xr n_fmt_charserrorrowrow2numbersrJrs r6r!r!nsx#uCjji  I........BF%%%!%  u B UD!!! V  $ $UD8 $ D D  B Yuh2( 3 3@AAAF JqMM 6Q;;!&1**@16IKK K Vq[[w}$M!$$&17=))71:Doa((  99u % %3xx4$%?#c((%JKKK9%%**3uc??;;FF S ! ! 9))C..KH3NOOEa))*-##sss36=CC'D.C",, T!:!: "  t(;(; *##788 8 v;;??^^D$/::F HHX&0 1 1 1   / /!--FKK ,,,KK ,,,,U4[[(724--....  /  Es+g5AA EEE#)'*17||||VVV%=>>CDEE v;;??^^D$/::F HHX&0 1 1 1   HHJJJJJ  6  HHJJJJ s21JQ N"!Q" O,)OOA QQ-cd}t|ds=tj|}tjj|d|}d} t|tjstj|}|j td| }t|tr%t|trt|}n9t|tr$t|trt|}t|dst!j|}||}|r^t|d t&sCtj||j d }tj|| }||_ntj|| }||r|SS#|r|wwxYw) aq Construct an array from a text file, using regular expression parsing. The returned array is always a structured array, and is constructed from all matches of the regular expression in the file. Groups in the regular expression are converted to fields of the structured array. Parameters ---------- file : path or file Filename or file object to read. .. versionchanged:: 1.22.0 Now accepts `os.PathLike` implementations. regexp : str or regexp Regular expression used to parse the file. Groups in the regular expression correspond to fields in the dtype. dtype : dtype or list of dtypes Dtype for the structured array; must be a structured datatype. encoding : str, optional Encoding used to decode the inputfile. Does not apply to input streams. .. versionadded:: 1.14.0 Returns ------- output : ndarray The output array, containing the part of the content of `file` that was matched by `regexp`. `output` is always a structured array. Raises ------ TypeError When `dtype` is not a valid dtype for a structured array. See Also -------- fromstring, loadtxt Notes ----- Dtypes for structured arrays can be specified in several forms, but all forms specify at least the data type and field name. For details see `basics.rec`. Examples -------- >>> from io import StringIO >>> text = StringIO("1312 foo\n1534 bar\n444 qux") >>> regexp = r"(\d+)\s+(...)" # match [digits, whitespace, anything] >>> output = np.fromregex(text, regexp, ... [('num', np.int64), ('key', 'S3')]) >>> output array([(1312, b'foo'), (1534, b'bar'), ( 444, b'qux')], dtype=[('num', '>> output['num'] array([1312, 1534, 444]) FrKrrTNz$dtype must be a structured datatype.matchrr)rMrrrrrr~rrr%rrKrrrrrecompilefindallrarrayrm) rPregexprrr]contentseqnewdtypeoutputs r6r*r*fs|F 4 yv!&&tTH&EE%** $HUOOE ; BCC C))++ gu % % #*VS*A*A #V__FF  % % #*VU*C*C #6]]Fvw'' (Z''FnnW%%  0z#a&%00 0xek!n 566HXc222F FLLXc///F   JJLLLL 6  JJLLLL s E;G$$G=_zf%i)rr1c |" STUVWXYZ[\|Qt||fidd|d|dd|d|d|d |d | d d | d | d| d|d|dd|d|d|d|d|d|d|St||&|rtd|dkrtd|rddlm}m}|pi}t |tstdt|z|dkrd}d }nd!}t |trt|}t |tr|>dkr&| r3 \fd>| D\n=#tp$r|fYWwxYw|>|&kr|fu|8t[\|r0|:t[d?t_\|DtEY|krndddn #1swxYwYtK|D]\V}?Vfd@YD}@ |?9|@*#tt$rdAVz}AtwtyVY}@tK|@D]Q\}4}, |?=|,#t|tf$r"|AdBz }A|A|4dzz|,fz}At}|AwxYwYwxYwtE|;}B|BdkrtEY|Bz|z WdC|&z[|dkr.tEWfdD|;D}C|;d|B|Cz };||Cz}[fdE|;D}AtE|ArX|A?ddFdG|A}A|rt|At;j|Atd*+|dkrYd| Y|r |9d| }9|r0tCt_YfdHtK|DYn/tCt_YfdItK|DYY}DdJ|D}EdKtK|EDZ|r\ZrZt;jdLtjAd*+ZfdMU UfdN|DD}DZD]VtjB|EV<n#t$rYnwxYw|Edd}FtK|ED]F\V}GtjD|GtjEr"tVfdO|DD}H|G|Hf|FV<G tdPt_||ED}ItE|Idkr|I\}J|Jt}L}KnfdQtK|FD}K|rfdRtK|FD}LnPtCt_ |F}KtCt_ tgtE|Fz}LtjH|D|KSX|rtjH|9|LS}Mn rj) _)tE|1dkrdTdU|1Dvr5trtdVtjH|DSXn5tjH|DdW|1DSYYKX|rRtjH|9tj(dX|1DS}N|}L|NK|L}Mn-|rd }OgTtKdY|DD]\V}PV|vrb|O|Pj kz}OtjD|PtjEr|PtVfdZ|DDf}PT0d(|PfkT0d(f|Os`_. Examples -------- >>> from io import StringIO >>> import numpy as np Comma delimited file with mixed dtype >>> s = StringIO(u"1,1.3,abcde") >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'), ... ('mystring','S5')], delimiter=",") >>> data array((1, 1.3, b'abcde'), dtype=[('myint', '>> _ = s.seek(0) # needed for StringIO example only >>> data = np.genfromtxt(s, dtype=None, ... names = ['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, b'abcde'), dtype=[('myint', '>> _ = s.seek(0) >>> data = np.genfromtxt(s, dtype="i8,f8,S5", ... names=['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, b'abcde'), dtype=[('myint', '>> s = StringIO(u"11.3abcde") >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'], ... delimiter=[1,3,5]) >>> data array((1, 1.3, b'abcde'), dtype=[('intvar', '>> f = StringIO(''' ... text,# of chars ... hello world,11 ... numpy,5''') >>> np.genfromtxt(f, dtype='S12,S12', delimiter=',') array([(b'text', b''), (b'hello world', b'11'), (b'numpy', b'5')], dtype=[('f0', 'S12'), ('f1', 'S12')]) Nrrr skip_header skip_footerrmissing_valuesfilling_valuesrr% excludelist deletechars replace_space autostripcase_sensitive defaultfmtrusemaskloose invalid_raiserrrzPThe keywords 'skip_footer' and 'max_rows' can not be specified at the same time.rz'max_rows' must be at least 1.r) MaskedArraymake_mask_descrzNThe input argument 'converter' should be a valid dictionary (got '%s' instead)rTFrrz\fname must be a string, a filehandle, a sequence of strings, or an iterator of strings. Got r)rrr{r)rxryr|rzrz"genfromtxt: Empty input file: "%s"r)rc6g|]}|SrIstriprrrs r6rzgenfromtxt..s AAA17799AAAr8rcPg|]#}t|$SrI)rrrs r6rzgenfromtxt..s&#I#I#IqC NN#I#I#Ir8c6g|]}|SrIrrs r6rzgenfromtxt..s #H#H#H!AGGII#H#H#Hr8)r}r%rxryr|rzc g|] }| SrIrI)rrrdescrs r6rzgenfromtxt..s!.s333aq333r8rIrc.g|]}tdgSr)rArs r6rzgenfromtxt..#s <<<$t**<<.9s///a3q66///r8c8g|]\}}td||S)N)rvdefaultr)rmissfills r6rzgenfromtxt..{sASSS*tT*$tTRRRSSSr8) flatten_basec<g|]\}}}t|d||ST)lockedrvrr)rr.rrs r6rzgenfromtxt..sI==="22tT.b=A4QQQ===r8c<g|]\}}td||Srr)rrrrs r6rzgenfromtxt..sH999".4.eD=A4QQQ999r8ct|tur ||S||dSNr)rrrL)rhconvs r6 tobytes_firstz!genfromtxt..tobytes_firsts<Aww%''#tAww4 2 2333r8)r)r testing_valuerrvc g|] }| SrIrI)rrrrs r6rzgenfromtxt..s999AfQi999r8c@g|]\}}||vSrIr)rrJms r6rzgenfromtxt..s>'J'J'J+1Aq()wwyyA~'J'J'Jr8c@g|]}t|SrI)r)r_mrs r6rzgenfromtxt..s)???BmjmmB//???r8z0Converter #%i is locked and cannot be upgraded: z"(occurred line #%i for value '%s')z- Line #%%i (got %%i columns instead of %i)c2g|]}|dzk|S)rrI)rrrnbrowsrts r6rzgenfromtxt..s9%E%E%E1()!v /C(C(C&'(C(C(Cr8c$g|] \}}||fz SrIrI)rrnbtemplates r6rzgenfromtxt.. s6***q"aW$***r8zSome errors were detected !r?cjg|].\}fdtt|D/S)c:g|]}|SrI) _loose_callr_rrs r6rz)genfromtxt... s'KKKB4##B''KKKr8rXrrrrrowss @r6rzgenfromtxt.. sS:::q$LKKK#jmmT2J2JKKK:::r8cjg|].\}fdtt|D/S)c:g|]}|SrI) _strict_callrs r6rz)genfromtxt... s'LLLR4$$R((LLLr8rrs @r6rzgenfromtxt.. sS:::q$MLLL3z!}}d3K3KLLL:::r8cg|] }|j SrIrrrs r6rzgenfromtxt.." s999d 999r8c8g|]\}}|tjk|SrI)rstr_)rrrJs r6rzgenfromtxt..$ s.&&&6AqRW $ r8zReading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.ct|}D] }||d||<!t|Sr)rArLr)row_tuprbr strcolidxs r6encode_unicode_colsz'genfromtxt..encode_unicode_cols. sD7mm"55A V]]844CFFSzz!r8c&g|] }|SrIrI)rrrs r6rzgenfromtxt..5 s%===1++A..===r8c3BK|]}t|VdSr0rzrrbrs r6 zgenfromtxt..@ s-::cc#a&kk::::::r8c&h|]\}}|j |SrI)_checked)rrc_types r6 zgenfromtxt..E s4Av:r8c$g|] \}}|z|f SrIrIrrr.r}s r6rzgenfromtxt..M s<HHH%q"&>2.HHHr8c.g|]\}}|ztfSrIboolrs r6rzgenfromtxt..P s<LLL")1b *A~t4LLLr8rhOc3$K|] }|jV dSr0)charrs r6rzgenfromtxt..b s$22!qv222222r8z4Nested fields involving objects are not supported...cg|]}d|fSrrIrs r6rzgenfromtxt..i s,I,I,Ib!W,I,I,Ir8c g|] }dtf Srr)rts r6rzgenfromtxt..n s*J*J*J!B:*J*J*Jr8cg|] }|j SrIrrs r6rzgenfromtxt..x s*L*L*L49*L*L*Lr8c3BK|]}t|VdSr0rrs r6rzgenfromtxt..} s-/L/LCF /L/L/L/L/L/Lr8c g|] }|tf SrIrrs r6rzgenfromtxt.. s===Aq$i===r8c2g|]}|dk|SrrI)rrrrs r6rzgenfromtxt.. s1***!!"b#d1gg!(r8rc g|] }| SrIrI)rrrqs r6rzgenfromtxt.. s555eF5M555r8)P_genfromtxt_with_likerrnumpy.marrrrrrrrrrrrr~rrrrwrrrangenextrrr StopIterationr"r#rr=rArzrrrrrrr%rrrrextendrcrbrr functoolspartialupdate itertoolschain IndexError iterupgraderrXrupgraderinsertrVisibleDeprecationWarningbytes_UnicodeEncodeError issubdtype charactermaxrrlrNotImplementedErrorviewrv_maskrr)]rrrrrtrurrvrwrr%rxryrzr{r|r}rr~rrrrrr1rruser_convertersr refid_ctxfhdr split_linevalidate_names first_values first_linefvalnbcolscurrentuser_missing_valuesr?rrrentry user_valueuser_filling_valuesn dtype_flatzipit uc_updaterr user_convrappend_to_rowsmasksappend_to_masksinvalidappend_to_invalidrnbvalues convertercurrent_columnerrmsg nbinvalidnbinvalid_skippedr) column_typessized_column_typescol_typen_charsbase uniform_typeddtypemdtype outputmaskrowmasks ishomogeneousttypermvalrrrrrrqrrrrs] ` ` ` ` @@@@@@@@@@r6r#r#sTF $ %    $u  /7x  CL9  #   1<  "z  3A.  *>  4;7  CH%  $  2=  (-  4=9  *>  7Aj  6  $+7  38%  (-  3;(  FNX  %  &e,,,  3233 3 a<<=>> >:99999999 &BO ot , ,: !#'#8#8 9:: :7%%%!%  %.f %%eTH%EE$S))(--3ii  E.25kk E E E    VV!I,5JJJ &;3>6D5BDDD  V;''  S  L" 6)$s))X>> TMM(<:--GGJ$4$4X$>$>qrr$BCC#)z*55 # 6 V V VJL M>FST U U U U U U  V D==?((**D#8##$Q   *AAgmmC.@.@AAA! * * **"7mmGG ***&kGGG* * W, -- D=="N#I#IL#I#I#IJJEJJ U # # *"N#H#Hu{{37G7G#H#H#HIIEE  *"N5))E  u5+6+6.<-: <<"J"J ( (E %%LL''' ( +S 1 1 9,22377J' ) ) Z(((( )( 9 9 c"56678888-  &"$ & )4 0 0 <17799 * * c"3''!!#kk#..%!!! !%mmC00%'*s## *"+dE] ; ; <'((AV %8rr""!4WfW!=22V;N =SS.1...Q.QSSSJJ'u4@@@J:""JGG==6;=== NN;;999927999  (..00% -% -IQq!!  AAAA!H  a((AA!H: % ,Q $ u}}#  !444&-m$GGG  qM 4/<)7):0>q0A ! D D D   a^ , , , ,y)))  +E#lO#N#9?J>3#G#GHH  IQZ%%F6{{H1}} 9999999FF!%%q;':H&EFFFHV##!!1{?Q#6"ABBB N5== ) ) ) L'J'J589G6I6I'J'J'J!K!KLLL4yyH$$%kVVVVVVVVVVVVVVVr }' 33 5 5NQ ????$???N 5%%n5555% 5 5 5KaO!$Z]]D!9!9"+N";";55JQ5!))%0000*J7555"FF1q5;#6">>,V444555 5G I1}}TY&4BVK ?? #%E%E%E%E%E%E%E%E!F!F !>D4yyA~~ $ $0$HHHH)23E)F)FHHHLLLLL-67I-J-JLLLF#e%78899F#edVc2D.E.E%EFFGGF$f---  7%v666J  U[,EK z??Q   22z22222$U++9-NPPP Xd%888FFx,I,Ij,I,I,IJJJ5)) 38*J*Jz*J*J*J!K!KMMM)//%]]622  0 $  )*L*L*L*L*L M M22HAuO++%%5:*=> = ==N%*C/L/L/L/Lt/L/L/L,L,L$ME b%[1111 b%[1111$05zzA~~ "!#XdE**F ;;*=====FF!FXe6::: L E;5;z22 ; ;LT4****t/B***N& ; ;4   VD\T%9:     ;"[))! "6 7 7 7F 6 =8O ZZ1__%(# #6555u555 5 Ms4F F4!F//F49'i!B I+*i+'JiJ,iK"!i" L-K=<L= L  L L  LiLH iT32i3 U=i?UiUi U)&i(U))Fi1\i \i\i\0/i0 \=:i<\==C;i9ai aiai$a:9i: bibDif&%i&gigA=iii?jAl%k,+l%,3l l%$l%s77 ttc |dd|dd}t|fi|}|rddlm}||}n|t j}|S)a Load ASCII data from a file and return it in a record array. If ``usemask=False`` a standard `recarray` is returned, if ``usemask=True`` a MaskedRecords array is returned. Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function Notes ----- By default, `dtype` is None, which means that the data-type of the output array will be determined from the data. rNr~Fr MaskedRecords) setdefaultgetr#numpy.ma.mrecordsrrrrecarray)rrRr~rqrs r6r$r$ s* gt$$$jjE**G  ( ( ( (F*333333]++R[)) Mr8c v|dd|dd|dd|ddt|fi|}|d d }|rd d lm}||}n|t j}|S) a8 Load ASCII data stored in a comma-separated file. The returned array is a record array (if ``usemask=False``, see `recarray`) or a masked record array (if ``usemask=True``, see `ma.mrecords.MaskedRecords`). Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function to load ASCII data. Notes ----- By default, `dtype` is None, which means that the data-type of the output array will be determined from the data. r|lowerr%TrrrNr~Frr)rr#rrrrrr)rrRrqr~rs r6r%r% s. &000 gt$$$ k3''' gt$$$  ( ( ( (FjjE**G*333333]++R[)) Mr8)NFTr)NN)TT)TN)r)NNNNNNN)r=r>r?rrr@Nr0)Urrirrr"r1rrrropindexrcollections.abcrr+rrrrr numpy.corer numpy.core.multiarrayr r numpy.core._multiarray_umathr numpy.core.overridesrr_iotoolsrrrrrrrrrrr numpy.compatrrrrrr __all__rarray_function_dispatchr.rSrUrr&rr'rr(rr)rrintrrrrr5r/floatr"r4r<r!r*rsorteddefaultdeletecharsr#rr$r%rIr8r6r0s4 ??????????############ 66666666<<<<<<HHHHHHHH     ,)+ %g777.B.B.B.B.B.B.B.Bb 2 2 2m@m@m@m@m@gm@m@m@` G?CyM.4.EyMyMyMyMyMx)**GHGHGH+*GHT *++S$S$,+S$l 566>#>#76>#B""""JEEE s    08888&"3cdaDU WUUUUUp GaegVCGVVVVVr/,,..w77EI;?!% ,--GI/3ttt.-tn G____N G!C4!"4t7766-*J#K#KLL E$e4!D7Z Z Z Z Z Z z2//11*==@#####r8